How to install query
npx skills add https://github.com/neolabhq/context-engineering-kit --skill queryFull instructions (SKILL.md)
Source of truth, from neolabhq/context-engineering-kit.
name: query description: "Search the FPF knowledge base and display hypothesis details with assurance information"
Query Knowledge
Search the FPF knowledge base and display hypothesis details with assurance information.
Action (Run-Time)
- Search
.fpf/knowledge/and.fpf/decisions/by user query. - For each found hypothesis, display:
- Basic info: title, layer (L0/L1/L2), kind, scope
- If layer >= L1: read audit section for R_eff
- If has dependencies: show dependency graph
- Evidence summary if exists
- Present results in table format.
Search Locations
| Location | Contents |
|---|---|
.fpf/knowledge/L0/ | Proposed hypotheses |
.fpf/knowledge/L1/ | Verified hypotheses |
.fpf/knowledge/L2/ | Validated hypotheses |
.fpf/knowledge/invalid/ | Rejected hypotheses |
.fpf/decisions/ | Design Rationale Records |
.fpf/evidence/ | Evidence and audit files |
Output Format
## Search Results for "<query>"
### Hypotheses Found
| Hypothesis | Layer | Kind | R_eff |
|------------|-------|------|-------|
| redis-caching | L2 | system | 0.85 |
| cdn-edge | L2 | system | 0.72 |
### redis-caching (L2)
**Title**: Use Redis for Caching
**Kind**: system
**Scope**: High-load systems, Linux only
**R_eff**: 0.85
**Weakest Link**: internal test (0.85)
**Dependencies**:
[redis-caching R:0.85] └── (no dependencies)
**Evidence**:
- ev-benchmark-redis-caching-2025-01-15 (internal, PASS)
### cdn-edge (L2)
**Title**: Use CDN Edge Cache
**Kind**: system
**Scope**: Static content delivery
**R_eff**: 0.72
**Weakest Link**: external docs (CL1 penalty)
**Evidence**:
- ev-research-cdn-2025-01-10 (external, PASS)
Search Methods
By Keyword
Search file contents for matching text:
/fpf:query caching
-> Finds all hypotheses with "caching" in title or content
By Specific ID
Look up a specific hypothesis:
/fpf:query redis-caching
-> Shows full details for redis-caching
-> Displays dependency tree
-> Shows R_eff breakdown
By Layer
Filter by knowledge layer:
/fpf:query L2
-> Lists all L2 hypotheses with R_eff scores
By Decision
Search decision records:
/fpf:query DRR
-> Lists all Design Rationale Records
-> Shows what each DRR selected/rejected
R_eff Display
For L1+ hypotheses, read the audit section and display:
**R_eff Breakdown**:
- Self Score: 1.00
- Weakest Link: ev-research-redis (0.90)
- Dependency Penalty: none
- **Final R_eff**: 0.85
Dependency Tree Display
If hypothesis has depends_on, show the tree:
[api-gateway R:0.80]
└──(CL:3)── [auth-module R:0.85]
└──(CL:2)── [rate-limiter R:0.90]
Legend:
R:X.XX= R_eff scoreCL:N= Congruence Level (1-3)
Examples
Search by keyword:
User: /fpf:query caching
Results:
| Hypothesis | Layer | R_eff |
|------------|-------|-------|
| redis-caching | L2 | 0.85 |
| cdn-edge-cache | L2 | 0.72 |
| lru-cache | invalid | N/A |
Query specific hypothesis:
User: /fpf:query redis-caching
# redis-caching (L2)
Title: Use Redis for Caching
Kind: system
Scope: High-load systems
R_eff: 0.85
Evidence: 2 files
Query decisions:
User: /fpf:query DRR
# Design Rationale Records
| DRR | Date | Winner | Rejected |
|-----|------|--------|----------|
| DRR-2025-01-15-caching | 2025-01-15 | redis-caching | cdn-edge |
Related skills
More from neolabhq/context-engineering-kit and the wider catalog.
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Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
context-engineering
Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
thought-based-reasoning
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
reflect
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
update-docs
Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.
multi-agent-patterns
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.